The first proposed method employs the Magnitude Fourier Spectrum (MFS) of an input image to detect an adversarial example. Unlike almost all existing detection methods, our first method does not need any access to the underlying network, it only depends on the input images. This simplicity provides many potential advantages, such as less computation time and better transferability properties. We evaluate our proposed method on different attacks, the early fast gradient sign method (FGSM) [1]}, two of its advances, the basic iterative method (BIM) [2]} and the projected gradient descent (PGD) [3]} and the Deepfool [4]} and Carlini & Wagner [5]} methods. Besides the effects of different attack methods we also investigate how an adversarial attack impacts different frequencies in the Fourier domain and what differences are in the phase and the magnitude spectrum. Our first method appears to be very successful on images attacked by FGSM, PGD, and BIM, but less successful on Deepfool and C&W.
